Automatic classification of weld cracks using artificial intelligence and statistical methods...

Automatic classification of weld cracks Automatic classification of weld cracks using artificial intelligence and using artificial intelligence and

statistical methodsstatistical methods

Ryszard SIKORA, Piotr BANIUKIEWICZ, Ryszard SIKORA, Piotr BANIUKIEWICZ, Marcin CARYKMarcin CARYK

Szczecin University of TechnologySzczecin University of Technology Department of Electrical and Department of Electrical and

Computer EngineeringComputer Engineering

uul. Sikorskiego 37l. Sikorskiego 37, 7, 70-313 Szczecin0-313 Szczecin

POLANDPOLAND

Szczecin, June 2008 2

OUTLINEOUTLINE Digital radiography systemDigital radiography system Automatic Defect Recognition algorithmAutomatic Defect Recognition algorithm Introduction,Introduction, ADDIPADDIP,, Data base preparation,Data base preparation, Statistical analysisStatistical analysis Artificial neural network classifierArtificial neural network classifier ConclusionsConclusions


DIGITAL RADIOGRAPHY SYSTEMDIGITAL RADIOGRAPHY SYSTEM

1. Portable X-Ray source (120KV, 1mA)2. Phosphor-plate scanner (spatial resolution 50μm,

digital resolution 16bit)3. Personal computer ( Pentium D 3,2 GHz, 2GB RAM)

1

2

3

DR

60

00

+ C

P12

0


AUTOMATIC DEFECT RECOGNITION ALGORITHMAUTOMATIC DEFECT RECOGNITION ALGORITHM

Radiograph acquisition

ROI Selection, IQI detection and evaluation

Contrast enhancement, normalization, noise reduction

Image segmentation

Defect detection & indexing

Feature extraction

Defect recognition

Acceptance algorithm


INTRODUCTIONINTRODUCTION The defects data base was prepared using ADDIP (developed by PS), The classification is done in accordance with respective welding norm EN ISO 6520-1 The statistical method PCA is applied in order to find redundant features, The artificial neural network was used as a defect group classifier, The real digital radiographs of welded parts of a ship were analyzed


ADDIPADDIP AAutomatic utomatic DDefect efect DDetection and etection and IIdentification dentification PProcessor (rocessor (ADDIPADDIP) ) is a collection of selected image processing algorithms dedicated for is a collection of selected image processing algorithms dedicated for automatic radiograph analysis. automatic radiograph analysis. The The ADDIPADDIP was created as a programming environment for quick was created as a programming environment for quick and easy testing of newly developed algorithms for defect and easy testing of newly developed algorithms for defect identification and recognition. identification and recognition.


DATA BASE PREPARATIONDATA BASE PREPARATION

Index defects Calculate features

Generate background Subtract background from

weld image

Detect angle and rotation Crop image Normalize image Detect ROI

PREPROCESING

DEFECT DETECTION

FEATURE CALCULATION

Algorithm of data base preparation using function implemented in ADDIP



Acquired radiograph image with defects

Image after rotation, crop and normalization operations



ROI region detected

Image cropped to ROI region and segmented Flaw 1 Flaw 3Flaw 2 Flaw 4


DATA BASE PREPARATIONDATA BASE PREPARATIONFlaw 1

Weld image Weld image - background

Thresholded imageIndex image



Weld image - background


Weld image


DATA BASE PREPARATIONDATA BASE PREPARATIONType of defects analyzed (according to EN-ISO-6520_1)

101 (6,3%), 102 (2,0%) - Cracks

Example image:

2011 (6,3%) - Porosity and gas pores, 2013 (15,0%) - Clustered porosity, 2015 (8,7%) - Elongated cavities, 2016 (12,8%) – wormholes

Example image:

3011 (17,4%), 3012 (12,8%) - Slag inclusions

Example image:


DATA BASE PREPARATIONDATA BASE PREPARATIONType of defects analyzed (according to EN-ISO-6520_1)

4011 (12,8%) - Lack of side wall fusion

Example image:

5011 (6,3 %) - Continuous undercut

Example image:


STATISTICAL ANALYSIS STATISTICAL ANALYSIS Database: over 400 cracks in 20 pictures from Technic-Control

Five groups according to EN-ISO 6520 norm

- Group 1 – cracks

- Group 2 – porosity and gas pores

- Group 3 – slag and inclusions

- Group 4 – lack of fusion, lack of penetration

- Group 5 – continuous undercut

Principal Components Analysis - a quantitatively rigorous method for achieving simplification of dimensionality of database.

Dimensionality of features space: 21

First eight principal components (PC) explain almost 100% of the total variability in the standardized ratings.

Cumulative sum


STATISTICAL ANALYSIS STATISTICAL ANALYSIS Data separation for eight PC


STATISTICAL ANALYSIS STATISTICAL ANALYSIS Factor analysis – finding redundant features

1 – Area2 – Perimeter3 – Center of gravity (x)4 – Center of gravity (y)5 – Center of gravity according to brightness (x)6 – Center of gravity according to brightness (y)7 – Longer diagonal of ellipse8 – Second diagonal of ellipse9 – Perpendicular diagonal to longer diagonal10 – Angle11 – Compactness12 – Anisometry13 – Elongation14 – Lengthening15 – Rectangularity16 – Mean Brightness17 – Max Dev of Brightness18 – Ratio19 – Heywood20 – Surroundings21 – Surroundings (mean brightness)

13

10

46

8

919

35

15

1

72121411

18

1620

2117

Visualization of the principal component coefficients for each feature

•Features 4 and 6 are linearly depended



•Features 16, 20 and 21 are linearly depended


ARTIFICAL NEURAL NETWORK CLASYFIERARTIFICAL NEURAL NETWORK CLASYFIER

Feature Vector

2015

0

1

0

0

0

Neuron of hidden layer

one hidden layer = 12 neurons

two hidden layers = [15 10] neurons

Neuron of output layer

Output layer = 5 neurons

• Two structures of neural networks were trained, with one hidden layer and with two hidden layer,

• Number of input corresponds to number of features,

• Number of inputs corresponds to number of defect group,

• The Levenberg-Marquardt optimization method was used as a network training function,

• The features database was randomly divided into three sets: 1) a training data set, 2) a validation data set and 3) testing data set.

Artificial neural network structure


ARTIFICAL NEURAL NETWORK CLASYFIERARTIFICAL NEURAL NETWORK CLASYFIER

In order to evaluate effectiveness of neural network classifier the mean square errorIn order to evaluate effectiveness of neural network classifier the mean square error between output vector and target vector was calculatedbetween output vector and target vector was calculated

21i i

i

MSE Y DN

where Yi – Output vectorDi – Target vectorN – number of samples in each defect

groupi – number of output neurons = 5

Defect group 1 Defect group 2 Defect group 3 Defect group 4 Defect group 5

Training data 0.0179 0.0008 0.0133 0.0068 0.4231

Validation data 1.0593 0.0178 0.2447 0.5181 0.3603

Testing data 1.3452 0.1161 0.7820 1.3122 0.4778

MSE obtained for neural network with two hidden layers


CONCLUSIONSCONCLUSIONS

• Cracks (group 1) are most separated defects group so easiest to detect,

• The most difficult to distinguish is defect group 4, which can be confused with first, second and third defect group,

• Small error obtained for training data and validation data confirms that the structure of applied NN has been chosen correctly,

• The best results of NN have been achieved for second, third and fifth group of defects, which are porosity and gas pores, slag and inclusions, undercuts respectively,

• Having suitable big training set, it is possible to build semi-automatic system distinguishing among main groups of imperfections

Automatic classification of weld cracks using artificial intelligence and statistical methods...

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